
"""simple docstring"""


import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


SCREAMING_SNAKE_CASE			  =  logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

SCREAMING_SNAKE_CASE			  =  {
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
SCREAMING_SNAKE_CASE			  =  {
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
SCREAMING_SNAKE_CASE			  =  sorted(arg_to_scheduler.keys())
SCREAMING_SNAKE_CASE			  =  """{""" + """, """.join(arg_to_scheduler_choices) + """}"""



class 	__a	(   pl.LightningModule						):






       def __init__(	self			:				List[Any]							,   UpperCAmelCase_			:				argparse.Namespace							,   UpperCAmelCase_			:				int=None							,   UpperCAmelCase_			:				Dict="base"							,   UpperCAmelCase_			:				Optional[Any]=None							,   UpperCAmelCase_			:				List[Any]=None							,   UpperCAmelCase_			:				str=None							,   **UpperCAmelCase_			:				Tuple							,   )->	List[Any]:







              """simple docstring"""
              super().__init__()
              # TODO: move to self.save_hyperparameters()
              # self.save_hyperparameters()
              # can also expand arguments into trainer signature for easier reading

              self.save_hyperparameters(UpperCAmelCase_      )
              UpperCamelCase       			=		0
              UpperCamelCase       			=		Path(self.hparams.output_dir      )
              UpperCamelCase       			=		self.hparams.cache_dir if self.hparams.cache_dir else None
              if config is None:
                     UpperCamelCase       			=		AutoConfig.from_pretrained(
                         self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path							,   **({"num_labels": num_labels} if num_labels is not None else {})							,   cache_dir=UpperCAmelCase_							,   **UpperCAmelCase_							,   )
              else:
                     UpperCamelCase       			=		config

              UpperCamelCase       			=		("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
              for p in extra_model_params:
                     if getattr(self.hparams							,   UpperCAmelCase_							,   UpperCAmelCase_      ):
                            assert hasattr(self.config							,   UpperCAmelCase_      ), f"model config doesn't have a `{p}` attribute"
                            setattr(self.config							,   UpperCAmelCase_							,   getattr(self.hparams							,   UpperCAmelCase_      )      )

              if tokenizer is None:
                     UpperCamelCase       			=		AutoTokenizer.from_pretrained(
                         self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,   cache_dir=UpperCAmelCase_							,   )
              else:
                     UpperCamelCase       			=		tokenizer
              UpperCamelCase       			=		MODEL_MODES[mode]
              if model is None:
                     UpperCamelCase       			=		self.model_type.from_pretrained(
                         self.hparams.model_name_or_path							,   from_tf=bool(".ckpt" in self.hparams.model_name_or_path      )							,   config=self.config							,   cache_dir=UpperCAmelCase_							,   )
              else:
                     UpperCamelCase       			=		model






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   *UpperCAmelCase_			:				Union[str, Any]							,   **UpperCAmelCase_			:				List[str]      )->	Optional[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.model_type.from_pretrained(*UpperCAmelCase_							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		arg_to_scheduler[self.hparams.lr_scheduler]
              UpperCamelCase       			=		get_schedule_func(
                  self.opt							,   num_warmup_steps=self.hparams.warmup_steps							,   num_training_steps=self.total_steps()      )
              UpperCamelCase       			=		{"scheduler": scheduler, "interval": "step", "frequency": 1}
              return scheduler






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		self.model
              UpperCamelCase       			=		["bias", "LayerNorm.weight"]
              UpperCamelCase       			=		[
                  {
                      "params": [
                          p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay      )
                      ],  # check this named paramters
                      "weight_decay": self.hparams.weight_decay,
                  },
                  {
                      "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay      )],
                      "weight_decay": 0.0,
                  },
              ]
              if self.hparams.adafactor:
                     UpperCamelCase       			=		Adafactor(
                         UpperCAmelCase_							,   lr=self.hparams.learning_rate							,   scale_parameter=UpperCAmelCase_							,   relative_step=UpperCAmelCase_      )

              else:
                     UpperCamelCase       			=		AdamW(
                         UpperCAmelCase_							,   lr=self.hparams.learning_rate							,   eps=self.hparams.adam_epsilon      )
              UpperCamelCase       			=		optimizer

              UpperCamelCase       			=		self.get_lr_scheduler()

              return [optimizer], [scheduler]






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]							,   UpperCAmelCase_			:				List[str]							,   UpperCAmelCase_			:				int      )->	Optional[Any]:







              """simple docstring"""
              return self.validation_step(UpperCAmelCase_							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str							,   UpperCAmelCase_			:				List[Any]      )->	Optional[Any]:







              """simple docstring"""
              return self.validation_end(UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		max(1							,   self.hparams.gpus      )  # TODO: consider num_tpu_cores
              UpperCamelCase       			=		self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
              return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]							,   UpperCAmelCase_			:				Optional[Any]      )->	Union[str, Any]:







              """simple docstring"""
              if stage == "test":
                     UpperCamelCase       			=		len(self.test_dataloader().dataset      )
              else:
                     UpperCamelCase       			=		self.get_dataloader("train"							,   self.hparams.train_batch_size							,   shuffle=UpperCAmelCase_      )
                     UpperCamelCase       			=		len(self.train_dataloader().dataset      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				bool = False      )->	List[str]:







              """simple docstring"""
              raise NotImplementedError("You must implement this for your task"      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	int:







              """simple docstring"""
              return self.train_loader






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	List[str]:







              """simple docstring"""
              return self.get_dataloader("dev"							,   self.hparams.eval_batch_size							,   shuffle=UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Union[str, Any]:







              """simple docstring"""
              return self.get_dataloader("test"							,   self.hparams.eval_batch_size							,   shuffle=UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str							,   UpperCAmelCase_			:				Any      )->	Optional[Any]:







              """simple docstring"""
              return os.path.join(
                  self.hparams.data_dir							,   "cached_{}_{}_{}".format(
                      UpperCAmelCase_							,   list(filter(UpperCAmelCase_							,   self.hparams.model_name_or_path.split("/"      )      )      ).pop()							,   str(self.hparams.max_seq_length      )							,   )							,   )






       @pl.utilities.rank_zero_only
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]							,   UpperCAmelCase_			:				Dict[str, Any]      )->	None:







              """simple docstring"""
              UpperCamelCase       			=		self.output_dir.joinpath("best_tfmr"      )
              UpperCamelCase       			=		self.step_count
              self.model.save_pretrained(UpperCAmelCase_      )
              self.tokenizer.save_pretrained(UpperCAmelCase_      )






       @staticmethod
       def 						_SCREAMING_SNAKE_CASE							(	UpperCAmelCase_			:				Optional[Any]							,   UpperCAmelCase_			:				List[Any]      )->	str:







              """simple docstring"""
              parser.add_argument(
                  "--model_name_or_path"							,   default=UpperCAmelCase_							,   type=UpperCAmelCase_							,   required=UpperCAmelCase_							,   help="Path to pretrained model or model identifier from huggingface.co/models"							,   )
              parser.add_argument(
                  "--config_name"							,   default=""							,   type=UpperCAmelCase_							,   help="Pretrained config name or path if not the same as model_name"      )
              parser.add_argument(
                  "--tokenizer_name"							,   default=UpperCAmelCase_							,   type=UpperCAmelCase_							,   help="Pretrained tokenizer name or path if not the same as model_name"							,   )
              parser.add_argument(
                  "--cache_dir"							,   default=str(Path(UpperCAmelCase_      ).parent / "test_run" / "cache"      )							,   type=UpperCAmelCase_							,   help="Where do you want to store the pre-trained models downloaded from huggingface.co"							,   )
              parser.add_argument(
                  "--encoder_layerdrop"							,   type=UpperCAmelCase_							,   help="Encoder layer dropout probability (Optional). Goes into model.config"							,   )
              parser.add_argument(
                  "--decoder_layerdrop"							,   type=UpperCAmelCase_							,   help="Decoder layer dropout probability (Optional). Goes into model.config"							,   )
              parser.add_argument(
                  "--dropout"							,   type=UpperCAmelCase_							,   help="Dropout probability (Optional). Goes into model.config"							,   )
              parser.add_argument(
                  "--attention_dropout"							,   type=UpperCAmelCase_							,   help="Attention dropout probability (Optional). Goes into model.config"							,   )
              parser.add_argument("--learning_rate"							,   default=5e-5							,   type=UpperCAmelCase_							,   help="The initial learning rate for Adam."      )
              parser.add_argument(
                  "--lr_scheduler"							,   default="linear"							,   choices=UpperCAmelCase_							,   metavar=UpperCAmelCase_							,   type=UpperCAmelCase_							,   help="Learning rate scheduler"							,   )
              parser.add_argument("--weight_decay"							,   default=0.0							,   type=UpperCAmelCase_							,   help="Weight decay if we apply some."      )
              parser.add_argument("--adam_epsilon"							,   default=1e-8							,   type=UpperCAmelCase_							,   help="Epsilon for Adam optimizer."      )
              parser.add_argument("--warmup_steps"							,   default=0							,   type=UpperCAmelCase_							,   help="Linear warmup over warmup_steps."      )
              parser.add_argument("--num_workers"							,   default=4							,   type=UpperCAmelCase_							,   help="kwarg passed to DataLoader"      )
              parser.add_argument("--num_train_epochs"							,   dest="max_epochs"							,   default=3							,   type=UpperCAmelCase_      )
              parser.add_argument("--train_batch_size"							,   default=32							,   type=UpperCAmelCase_      )
              parser.add_argument("--eval_batch_size"							,   default=32							,   type=UpperCAmelCase_      )
              parser.add_argument("--adafactor"							,   action="store_true"      )





class 	__a	(   pl.Callback						):






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				List[Any]      )->	List[Any]:







              """simple docstring"""
              if (
                  trainer.is_global_zero and trainer.global_rank == 0
              ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
                     pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.





class 	__a	(   pl.Callback						):






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				List[str]      )->	Any:







              """simple docstring"""
              # print(pl_module.model.rag)
              for name, param in pl_module.model.rag.named_parameters():
                     if param.grad is None:
                            print(UpperCAmelCase_      )





class 	__a	(   pl.Callback						):






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int      )->	Union[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		trainer.lr_schedulers[0]["scheduler"]
              UpperCamelCase       			=		{f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()      )}
              pl_module.logger.log_metrics(UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   UpperCAmelCase_			:				pl.Trainer							,   UpperCAmelCase_			:				pl.LightningModule      )->	Optional[Any]:







              """simple docstring"""
              rank_zero_info("***** Validation results *****"      )
              UpperCamelCase       			=		trainer.callback_metrics
              # Log results
              for key in sorted(UpperCAmelCase_      ):
                     if key not in ["log", "progress_bar"]:
                            rank_zero_info("{} = {}\n".format(UpperCAmelCase_							,   str(metrics[key]      )      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				pl.Trainer							,   UpperCAmelCase_			:				pl.LightningModule      )->	Union[str, Any]:







              """simple docstring"""
              rank_zero_info("***** Test results *****"      )
              UpperCamelCase       			=		trainer.callback_metrics
              # Log and save results to file
              UpperCamelCase       			=		os.path.join(pl_module.hparams.output_dir							,   "test_results.txt"      )
              with open(UpperCAmelCase_							,   "w"      ) as writer:
                     for key in sorted(UpperCAmelCase_      ):
                            if key not in ["log", "progress_bar"]:
                                   rank_zero_info("{} = {}\n".format(UpperCAmelCase_							,   str(metrics[key]      )      )      )
                                   writer.write("{} = {}\n".format(UpperCAmelCase_							,   str(metrics[key]      )      )      )





def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_					)->				None:







       """simple docstring"""
       #  To allow all pl args uncomment the following line
       #  parser = pl.Trainer.add_argparse_args(parser)
       parser.add_argument(
           "--output_dir"		,   default=str(Path(UpperCAmelCase_					).parent / "test_run" / "model_checkpoints"					)		,   type=UpperCAmelCase_		,   help="The output directory where the model predictions and checkpoints will be written."		,   )
       parser.add_argument(
           "--fp16"		,   action="store_true"		,   help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"		,   )

       parser.add_argument(
           "--fp16_opt_level"		,   type=UpperCAmelCase_		,   default="O2"		,   help=(
               "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
               "See details at https://nvidia.github.io/apex/amp.html"
           )		,   )
       parser.add_argument("--n_tpu_cores"		,   dest="tpu_cores"		,   type=UpperCAmelCase_					)
       parser.add_argument("--max_grad_norm"		,   dest="gradient_clip_val"		,   default=1.0		,   type=UpperCAmelCase_		,   help="Max gradient norm"					)
       parser.add_argument("--do_train"		,   action="store_true"		,   help="Whether to run training."					)
       parser.add_argument("--do_predict"		,   action="store_true"		,   help="Whether to run predictions on the test set."					)
       parser.add_argument(
           "--gradient_accumulation_steps"		,   dest="accumulate_grad_batches"		,   type=UpperCAmelCase_		,   default=1		,   help="Number of updates steps to accumulate before performing a backward/update pass."		,   )
       parser.add_argument("--seed"		,   type=UpperCAmelCase_		,   default=42		,   help="random seed for initialization"					)
       parser.add_argument(
           "--data_dir"		,   default=str(Path(UpperCAmelCase_					).parent / "test_run" / "dummy-train-data"					)		,   type=UpperCAmelCase_		,   help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."		,   )



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_=None		,   UpperCAmelCase_=True		,   UpperCAmelCase_=[]		,   UpperCAmelCase_=None		,   UpperCAmelCase_=None		,   **UpperCAmelCase_		,   )->				str:







       """simple docstring"""
       pl.seed_everything(args.seed					)

       # init model
       UpperCamelCase       			=		Path(model.hparams.output_dir					)
       odir.mkdir(exist_ok=UpperCAmelCase_					)

       # add custom checkpoints
       if checkpoint_callback is None:
              UpperCamelCase       			=		pl.callbacks.ModelCheckpoint(
                  filepath=args.output_dir		,   prefix="checkpoint"		,   monitor="val_loss"		,   mode="min"		,   save_top_k=1					)
       if early_stopping_callback:
              extra_callbacks.append(UpperCAmelCase_					)
       if logging_callback is None:
              UpperCamelCase       			=		LoggingCallback()

       UpperCamelCase       			=		{}

       if args.fpaa:
              UpperCamelCase       			=		16

       if args.gpus > 1:
              UpperCamelCase       			=		"auto"
              UpperCamelCase       			=		"ddp"

       UpperCamelCase       			=		args.accumulate_grad_batches
       UpperCamelCase       			=		None
       UpperCamelCase       			=		"auto"

       UpperCamelCase       			=		pl.Trainer.from_argparse_args(
           UpperCAmelCase_		,   weights_summary=UpperCAmelCase_		,   callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]		,   logger=UpperCAmelCase_		,   val_check_interval=1		,   num_sanity_val_steps=2		,   **UpperCAmelCase_		,   )

       if args.do_train:
              trainer.fit(UpperCAmelCase_					)

       else:
              print("RAG modeling tests with new set functions successfuly executed!"					)
       return trainer







"""simple docstring"""


import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()



class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       Any				=      VQModel
       UpperCamelCase_		:       Dict				=      '''sample'''






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				List[str]=(32, 32)      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		4
              UpperCamelCase       			=		3

              UpperCamelCase       			=		floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCAmelCase_      )

              return {"sample": image}






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	List[Any]:







              """simple docstring"""
              return (3, 32, 32)






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Tuple:







              """simple docstring"""
              return (3, 32, 32)






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		{
                  "block_out_channels": [32, 64],
                  "in_channels": 3,
                  "out_channels": 3,
                  "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
                  "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
                  "latent_channels": 3,
              }
              UpperCamelCase       			=		self.dummy_input
              return init_dict, inputs_dict






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	List[Any]:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	str:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase       			=		VQModel.from_pretrained("fusing/vqgan-dummy"							,   output_loading_info=UpperCAmelCase_      )
              self.assertIsNotNone(UpperCAmelCase_      )
              self.assertEqual(len(loading_info["missing_keys"]      )							,   0      )

              model.to(UpperCAmelCase_      )
              UpperCamelCase       			=		model(**self.dummy_input      )

              assert image is not None, "Make sure output is not None"






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		VQModel.from_pretrained("fusing/vqgan-dummy"      )
              model.to(UpperCAmelCase_      ).eval()

              torch.manual_seed(0      )
              if torch.cuda.is_available():
                     torch.cuda.manual_seed_all(0      )

              UpperCamelCase       			=		torch.randn(1							,   model.config.in_channels							,   model.config.sample_size							,   model.config.sample_size      )
              UpperCamelCase       			=		image.to(UpperCAmelCase_      )
              with torch.no_grad():
                     UpperCamelCase       			=		model(UpperCAmelCase_      ).sample

              UpperCamelCase       			=		output[0, -1, -3:, -3:].flatten().cpu()
              # fmt: off
              UpperCamelCase       			=		torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]      )
              # fmt: on
              self.assertTrue(torch.allclose(UpperCAmelCase_							,   UpperCAmelCase_							,   atol=1e-3      )      )





